Alignment of Custom Standards by Machine Learning Algorithms

• Main Authors: Adela Sirbu, Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pecuchet
• Format: Article
• Language: English
• Published: Babes-Bolyai University, Cluj-Napoca 2010-09-01
• Series: Studia Universitatis Babes-Bolyai: Series Informatica
• Online Access: http://www.cs.ubbcluj.ro/apps/reviste/index.php/studia-i/article/view/14

Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.